add file names and update evaluation scripts

### Stage-1: Prompting for Knowledge Generation

1. The script [`tasks/knwl_dialo/scripts/prompt_knwl_gen.sh`](./scripts/prompt_knwl_gen.sh) provides an example for how to perform the first-stage prompting for the knowledge generation.

2. The F1/FK1 score can be evaluated through [`tasks/knwl_dialo/scripts/eval_generation.sh`](./scripts/eval_generation.sh). Other automatic metrics (i.e., BLEU, METEOR, and ROUGE-L) follow the [nlg-eval](https://github.com/Maluuba/nlg-eval).

2. We provide the script [`tasks/knwl_dialo/scripts/eval_knwl_generation.sh`](./scripts/eval_generation.sh) for the automatic evaluation (i.e., F1, BLEU, METEOR, and ROUGE-L) of the knowledge generation.

### Stage-2: Prompting for Response Generation

1. The script [`tasks/knwl_dialo/scripts/prep_resp_gen.sh`](./scripts/prep_resp_gen.sh) helps to prepare the input file for the response generation (based on the previously generated knowledge file).

2. The script [`tasks/knwl_dialo/scripts/prompt_resp_gen.sh`](./scripts/prompt_resp_gen.sh) provides an example for how to perform the second-stage prompting for the response generation.

3. The automatic evaluations are the same as mentioned aboved for the knowledge generation.

3. We provide the script [`tasks/knwl_dialo/scripts/eval_resp_generation.sh`](./scripts/eval_generation.sh) for the automatic evaluation (i.e., F1, KF1, BLEU, METEOR, and ROUGE-L) of the knowledge generation.

#!/bin/bash

# This script is used to evaluate the F1 or KF1 scores.

#########################

# Evaluate the F1 scores.

#########################

WORLD_SIZE=1

DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \

                  --nnodes 1 \

                  --node_rank 0 \

                  --master_addr localhost \

                  --master_port 6000"

OUTPUT_PATH=<PATH_OF_THE_OUTPUT_GENERATION>

GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH>

MODEL_GEN_PATH=<PATH_OF_THE_KNOWLEDGE_GENERATION> \ 

        (e.g., /testseen_knowledge_generations.txt)

GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE> \ 

        (e.g., /testseen_knowledge_reference.txt)

python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \

        --num-layers 24 \

        --max-position-embeddings 2048 \

        --micro-batch-size 4 \

        --task KNWL-DIALO-EVAL-F1 \

        --guess-file ${OUTPUT_PATH} \

        --guess-file ${MODEL_GEN_PATH} \

        --answer-file ${GROUND_TRUTH_PATH}

############################################

# Evaluate BLEU, METEOR, and ROUGE-L scores.

############################################

# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to 

# evaluate the BLEU, METEOR, and ROUGE-L scores. 

# To evaluate on these metrics, please setup the environments based on 

# the nlg-eval github, and run the corresponding evaluation commands.

nlg-eval \

    --hypothesis=<PATH_OF_THE_KNOWLEDGE_GENERATION> \

    --references=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE>

#!/bin/bash

#########################

# Evaluate the F1 scores.

#########################

WORLD_SIZE=1

DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \

                  --nnodes 1 \

                  --node_rank 0 \

                  --master_addr localhost \

                  --master_port 6000"

MODEL_GEN_PATH=<PATH_OF_THE_RESPONSE_GENERATION> \ 

        (e.g., /testseen_response_generations.txt)

GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_RESPONSE> \ 

        (e.g., /testseen_response_reference.txt)

python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \

        --num-layers 24 \

        --hidden-size 1024 \

        --num-attention-heads 16 \

        --seq-length 2048 \

        --max-position-embeddings 2048 \

        --micro-batch-size 4 \

        --task KNWL-DIALO-EVAL-F1 \

        --guess-file ${MODEL_GEN_PATH} \

        --answer-file ${GROUND_TRUTH_PATH}

##########################

# Evaluate the KF1 scores.

##########################

MODEL_GEN_PATH=<PATH_OF_THE_RESPONSE_GENERATION> \ 

        (e.g., /testseen_response_generations.txt)

GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE> \ 

        (e.g., /testseen_knowledge_reference.txt)

python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \

        --num-layers 24 \

        --hidden-size 1024 \

        --num-attention-heads 16 \

        --seq-length 2048 \

        --max-position-embeddings 2048 \

        --micro-batch-size 4 \

        --task KNWL-DIALO-EVAL-F1 \

        --guess-file ${MODEL_GEN_PATH} \

        --answer-file ${GROUND_TRUTH_PATH}

############################################

# Evaluate BLEU, METEOR, and ROUGE-L scores.

############################################

# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to 

# evaluate the BLEU, METEOR, and ROUGE-L scores. 

# To evaluate on these metrics, please setup the environments based on 

# the nlg-eval github, and run the corresponding evaluation commands.

nlg-eval \

    --hypothesis=<PATH_OF_THE_RESPONSE_GENERATION> \

    --references=<PATH_OF_THE_GROUND_TRUTH_RESPONSE>

DIR=`pwd`

TEST_FILE=<PATH_OF_THE_PROCESSED_TEST_DATA>

KNOWLEDGE_FILE=<PATH_OF_THE_GENERATED_KNOWLEDGE_DATA>

PROCESSED_FILE=<PATH_OF_THE_INPUT_FILE_FOR_RESPONSE_GENERATION>

TEST_FILE=<PATH_OF_PROCESSED_TEST_DATA> \

        (e.g., /testseen_processed.txt)

KNOWLEDGE_FILE=<PATH_OF_GENERATED_KNOWLEDGE_DATA> \

        (e.g., /testseen_knowledge_generations.txt)

PROCESSED_FILE=<PATH_OF_INPUT_FILE_FOR_RESPONSE_GENERATION> \

        (e.g., /testseen_processed_with_generated_knowledge.txt)

python ${DIR}/tasks/knwl_dialo/preprocessing.py \

        --func prepare_input \

                  --master_addr localhost \

                  --master_port 6000"

CHECKPOINT_PATH=<PATH_OF_THE_LANGUAGE_MODEL>

INPUT_PATH=<PATH_OF_THE_INPUT_TEST_DATA_FILE>

PROMPT_PATH=<PATH_OF_THE_KNOWLEDGE_GENERATION_PROMPTS>

VOCAB_PATH=<PATH_OF_THE_VOCAB_FILE>

MERGE_PATH=<PATH_OF_THE_MERGE_FILE>

OUTPUT_PATH=<PATH_OF_THE_OUTPUT_GENERATION_FILE>

CHECKPOINT_PATH=<PATH_OF_LANGUAGE_MODEL> (e.g., /357m)

VOCAB_PATH=<PATH_OF_VOCAB_FILE> (e.g., /gpt2-vocab.json)

MERGE_PATH=<PATH_OF_MERGE_FILE> (e.g., /gpt2-merges.txt)

INPUT_PATH=<PATH_OF_PROCESSED_TEST_DATA_FILE> (e.g., /testseen_processed.txt)

PROMPT_PATH=<PATH_OF_KNOWLEDGE_GENERATION_PROMPTS> \

        (e.g., /testseen_knowledge_prompts.json)

OUTPUT_PATH=<PATH_OF_OUTPUT_GENERATION_FILE> \

        (e.g., /testseen_knowledge_generations.txt)

python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \

        --num-layers 24 \